#Predictive vs. Adaptive Development

[perfectiongeeks]

Predictive vs. Adaptive Development: Which Way to Go?

Predictive development, also known as traditional or waterfall development, is a linear and sequential approach to software development. In this method, the entire project is planned and defined in advance, with a detailed project roadmap outlining all the tasks, milestones, and deadlines. This methodology assumes that the project requirements can be clearly defined from the beginning and do not change significantly during the development process.

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[logiclitz]

Google Launches Android 16 Beta 1: Everything You Need to Know

Google has officially unveiled Android 16 Beta 1, marking a significant leap in its next-generation operating system. Designed to enhance adaptability, functionality, and inclusivity, this beta update is now available as an over-the-air (OTA) update for users enrolled in the Android Beta Program. Here’s everything you need to know about the exciting features, updates, and how it stacks up for…

[tanadrin]

Can you explain in what what you think eugenics doesn't work? Does this basically boil down to skepticism about the accuracy of GWAS studies? My understanding is that academic consensus is "G probably exists, disentangling direct genetic inheritance vs genetic cultural inheritance is complicated but possible, we can identify a number of alleles which we're reasonably confident are directly causally involved in having a higher G factor"

when it comes to intelligence, its heritability, and its variation at the population level, my understanding of the science is:

highly adaptive traits don't, in fact, vary much at the genetic level between populations of a species because they are strongly selected for. in an environment where a trait is being strongly selected for, a population that failed to express that trait strongly will be rapidly outcompeted.

intelligence is probably the quintessential such trait for humans. we have sacrificed a great deal of other kinds of specialization in favor of our big brains. we spend an enormous amount of calories supporting those brains. tool use, the ability to plan for the future, the ability to navigate complex social situations and hierarchies in order to secure status, the ability to model the minds of others for the purposes of cooperation and deception means that we should expect intelligence to be strongly selected for for as long as our lineage has been social and tool-using, which is at least the last three million years or so.

so, at least as a matter of a priori assumptions, we should expect human populations not to vary greatly in their genetic predisposition to intelligence. it may nonetheless, but we'd need pretty strong evidence. i think i read this argument on PZ Myers' blog a million years ago, so credit where that's due.

complicating the picture is that we just don't have good evidence for how IQ does vary across populations, even before we get into the question of "how much of this variation is genetic and how much of it is not." the cross-national data on which a lot of IQ arguments have been based is really bad. and that would be assuming IQ tests are in fact good at capturing a notion of IQ that is independent of cultural context, which historically they're pretty bad at

this screed by nassim nicholas taleb (not a diss; AFAICT the guy only writes in screeds) makes a number of arguments, but one argument I find persuasive is that IQ is really only predictive of achievement in the sense that it does usefully discriminate between people with obvious intellectual disabilities and those without--but you do not actually need an IQ test for that sort of thing, any more than you need to use a height chart to figure out who is missing both their legs. in that sense, sure, IQ is predictive of a lot of things. but once you remove this group, the much-vaunted correlations between IQ and stuff like wealth just straight-up vanishes

heritability studies are a useful tool, but a tool which must be wielded carefully; they were developed for studying traits which were relatively easy to isolate in very specific populations, like a crop under study at an agricultural research site, and are more precarious when applied to, e.g., human populations

my understanding based on jonathan kaplan articles like this one is that twin studies are not actually that good at distinguishing heritable factors from environmental ones--they have serious limitations compared to heritability studies where you actually can rigorously control for environmental effects, like you can with plants or livestock.

as this post also points out, heritability studies also only examine heritability within groups, and are not really suited to examining large-scale population differences, *especially* in the realm of intelligence where there is a huge raft of confounding factors, and a lack of a really robust measurement tool.

(if we are worried about intelligence at the population level, it seems to me there are interventions we know are going to be effective and do not rely on deeply dubious scientific speculation, e.g., around nutrition and healthcare and serious wealth inequality and ofc education; and if what people actually want is to raise the average intelligence of the population rather than justify discrimination against minorities, then they might focus on those much more empirically grounded interventions. even if population differences in IQ are real and significant and point to big differences in intelligence, we know those things are worth a fair few IQ points. but most people who are or historically have been the biggest advocates for eugenics are, in my estimation, mostly interested in justifying discrimination.)

i think the claims/application of eugenics extend well beyond just intelligence, ftr. eugenics as an ideology is complex and historically pretty interesting, and many eugenicists have made much broader claims than just "population-level differences in intelligence exist due to genetic factors, and we should try to influence them with policy," but that is a useful point for them to fall back onto when pressed on those other claims. but i don't think even that claim is at all well-supported.

[howtofightwrite]

Do you have any tips for how a smaller person should look for advantages while fighting against someone bigger than them? Both in height and weight (from a normal human perspective and, if possible, also from like, as an example, an average human man against a nine foot tall humanoid).

I know I'm going to sound like a broken record here, because we say it a lot, but having a lower center of gravity is a significant advantage in its own right. It makes throwing or knocking down your opponent much easier, and conversely, makes disrupting your own stance much harder.

The theoretical advantages you'll often see dragged out, like, “greater reach,” or, “superior strength,” don't really matter in a human vs. human match-up, because the differences aren't really that pronounced.

Someone who's nine feet tall is a little more significant. Match-ups like that are really hard to generalize in abstract because a lot of assumptions don't necessarily hold true. If the humanoid has significantly longer reach (which is likely) that could pose a real problem for any unarmed combat. Similarly center of gravity and strength could be significantly different from what you'd expect in a human. When you remember you're talking about a being that is 50% larger than you are, the problems start stacking up. You're talking about someone who will have over a foot of reach on you. If their physiology is exactly the same, and joint locks work without meaningful adaptation, that might give you some options, but the less human their physiology is, the harder that prediction becomes. For example: If they have a structure more like a gorilla, with very heavy, and significantly longer, forelimbs, that fight could become completely unwinnable. (At least, without developing specific martial arts to deal with that foe.

A joke I've made in the past is, “there no martial art designed for fighting bears,” but when you're dealing with non-human combatants, that absence would become a real problem.

-Starke

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[theladyheroine]

Predictions for the Sonic Cinematic Universe! 💙

❥ Hey everyone! So this post will be similar to my TMNT version, where I’ll just be guessing characters that could come in later movies or TV shows. Thank you & I hope you enjoy!

Silver the Hedgehog 🤍

Silver’s time to shine! Hopefully (lol) but I really hope he’s in the series soon! Since he’s one of the main members of the Hedgehog trio, as well as one of the protagonist in a major Sonic game, I feel he’ll fit in just fine! While I understand his debut didn’t go so well back in 2006, I think he has a big enough fan base to back him up too. Plus, Silver’s just so sweet & cool!

A time-traveling event would be such a great concept for a big film! Similar to the Rise of the TMNT movie, Silver could be an Older! Sonic’s apprentice from the future. Maybe something goes wrong and Silver has to travel back in time to fix it or warn Sonic.

As for portrayals, I like Silver the way he is but since he doesn’t show up too often, there could be ways to develop his personality a bit. While he’s very friendly, maybe making him shy or a little more hesitant than the other characters would fit. Since he might not have alot of heroic experience, Sonic might take on another mentor-ish/big brother role.

Rouge the Bat 💎

I know everyone wants Rouge in the Sonic movies and I do too! While she didn’t show up in the Knuckles series, she would make a great deuteragonist in the show later on! Or as a potential side character in the films! Although she would be a funny contrast to Knuckles with his stubborn, intimidating nature vs her laidback attitude.

Much similar to Shadow, posing as a former antagonist would do well for her, but Rogue feels more of a “mean girl” character than a big bad villain. Especially given her recent history in Sonic media. At the very least, she could be a jewel thief or treasure hunter who’s looking for the Master Emerald, but then decides to help everyone instead.

Continuing the “mean girl” perspective, a Movie version of Rouge would likely be interested in—well—her own interests. 😅 She knows what she wants & goes after it, but if it’s unavailable she might throw a fit… This could balance out Knuckles though if they interact, since he’s a lot more grounded but very loyal to everyone. Especially his allies!

Team Chaotix 🔎

These are characters I’m also very excited for! Ecstatic even! But since they come as a trio, there’s more to work with in adapting them for a film or TV show. Although they did have a cameo in the prequel comics! So hopefully that’s a sign!

Story-wise I think it would be a good opportunity to reunite the Chaotix with Knuckles! Plus they have some big in-game history with him in their first game, so it would be cool to have some sort of big mystery for them all to solve! Wade could tag along too & maybe the Chaotix become official detectives in Green Hills.

For personalities I think they would all be relatively the same, but I would LOVE to see Vector get into old noir films! Maybe try a few funny quips from the 1940s, only to say them wrong. 😅

Espio would be the type of character to just pop up out of no where all the time, like a true ninja! I also want to hear him with a Japanese voice actor, that I feel is just very fitting.

Since Charmy is already a hyperactive character, his energy could be a good contrast to Tails since they’re around the same age. Plus you got Colleen O'Shaughnessey doing two characters!

Tikal 🧡

Now I was a little unsure about Tikal appearing in the SCU considering she doesn’t appear very often & doesn’t reoccur either. But this could give her a chance to shine! Since she has big ties with Knuckles in the games, I think she could have at the very least a cameo or some kind of mention.

Her portrayal would be more fitting in the Knuckles TV show if they ever did a season 2 maybe. Since it mostly focuses on him, Tikal could be another ancestral spirit. Or if they really wanted to go big she could be another tribe member looking for Echidnas! That would be awesome!

Now all I really know about Tikal personality-wise is that she’s gentle & sweet! Her presence would be a nice addition to the cast; someone calming since the others can be very “upfront” with their abilities. Especially Sonic & Knuckles!

Thank you everyone for listening! Have a good day/night! ☀️🌖

[voxofthevoid]

In light of JJK 239, I am once again obsessing over Kenjaku and Yuuji—not that I ever really stopped.

What I want out of Sukuna vs. Yuuji is for Yuuji to tear a motherfucker apart, but with Kenjaku and Yuuji, I’m desperate for a conversation. Not in the sense of some wildly jarring parent–son heart-to-heart, but a clash of ideals in verbal form, like what we got out of the Mahito��Yuuji interactions.  

Yuuji, probably because of some blend of how he was raised with no knowledge of jujutsu and just who he is as a person, clearly views cursed energy as a means to an end. He wants to help people, and jujutsu helps him do that. Even prior to his cog mentality (and I’d love to see how that has changed or evolved, if it has, after Sukuna left his body), he seems to view what he can do with cursed energy as an extension of what he can do with his body. He’s good at it, he’s proud of it, and he has a truly impressive growth rate, but his talents are innate and his skills develop organically; I don’t get the impression that he views jujutsu itself as something to explore for its own sake. Essentially, he’s someone who’ll get stronger for the sake of the results, not the methods.

And then you have Kenjaku:

Yuuji might have inherited his insane adaptability from his mum (though an argument can be made for Itadori “Yeah That’s My Dead Wife, What Of It” Jin too), but he sure as shit doesn’t have their scientific curiosity. I think Kenjaku’s line of thinking—more specifically, its consequences—would strike Yuuji as frankly perverse.

I don’t have nearly enough of a handle on Kenjaku’s character to predict how they’d react, or even the extent and nature of their current interest in Yuuji, but some of my favorite panels from the Shibuya arc are the Kenjaku–Yuuji fight scenes, where Kenjaku kind of just…talks at Yuuji, like they’re giving him a fun educational lecture while actively traumatizing him.

I’d sacrifice a few body parts—not mine, but semantics—to see another encounter like that between these two, featuring their wildly different attitudes toward humanity as a whole.

[kaiserposting]

do you really think shidou will score or is it a joke

If we're talking about what I think will happen, no I don't think Shidou will actually score, I believe he has the lowest likelihood to score out of the 4 strikers on the field

But I would genuinely like to see that happen. 1. because it's actually unexpected (I was born for shock value btw), 2. because I feel like everyone has been revolving around Isagi-Kaiser-Rin so I feel like him coming out of nowhere to score would teach them a lesson (be mindful of why you were invited to pxg vs bm), 3. Because I feel like any one of those 3 scoring has weird implications for thier character development

Also I'm not against Kaiser and Isagi linking up we all knew that was gonna happen anyways and I think that's good, but on the other hand having 100+ chapters of beef just to settle with "let luck decide" is just really stupid?

And os on that, If Kaiser gets the lucky goal, that gives him a second goal that's like 50% because of luck and I think that's kinda stupid and unsatisfactory overall, and if Isagi gets the lucky goal I don't really know what that means for him because this is a trial of "originality as a striker" as Ego calls it but I just don't find what he has been doing all that original? He just saw Kaiser get over his mental breakdown and was like "Wait let me hop on that 😜" or at least that's how I read it and that's just weird to me I don't feel like he is making any real progress in mentality because he is basically always doing this anyways due to his nature of being super adaptable (+ not to mention Kaiser was taking Ls and humiliation rituals across several volumes meanwhile in this match Isagi basically just felt mildly stuck for a second and was also briefly sad that his oshi dgaf about him... which doesn't feel all that monumental to me). Isagi needs to See Others so he can Be, which is fine imo and I like him but this arc's trial is about originality so like ?

As for Rin he just hasn't really been pressed imo. I mean yes he felt as if he lost to Isagi after U-20 and Isagi managed to block him this time but I just feel like skill-wise Rin is a Little artist north and I think it's about time someone ACTUALLY wins against him skillwise (hence why I want Shidou to score even tho that will never happen)

As for what I think might happen, my prediction is that Kaiser and Isagi will be passing the ball back and forth and around the box it will be a similar situation like from chapter 1 in that match Isagi lost in hs, and for a second he might think to give in and pass to Kaiser, but then he's like NO..... I WANT... TO WIN!!!! and scores himself instead. But this is just my prediction so it could also mean nothing

In the case that Kaiser scores, I think Isagi will realize it was because his originality hasn't been up to par and take it as a learning opportunity

[creatureofmimics]

BREAKING NEWS: Revolutionary AI Scheduling Model Disrupts Multi-Billion Dollar Industries

IndustriesMay 14, 2025 – In a groundbreaking development set to redefine time management across education, fitness, and the arts, researchers from the Zurich Institute of Technology (ZIT) have unveiled an artificial intelligence model capable of autonomously optimizing human schedules with an accuracy rate exceeding 98.7%.Dubbed "ChronoPilot," the new system leverages deep contextual learning to interpret not only user preferences but also emotional states, productivity trends, and even regional weather forecasts to dynamically tailor personal and group schedules. In early trials, the system outperformed existing scheduling platforms by over 400% in efficiency and conflict resolution.The implications span vast sectors. Educational institutions using class scheduling software like Lunacal’s platform for class bookings have already reported dramatic improvements in attendance and engagement. In the fitness industry, where applications such as gym booking software are crucial, ChronoPilot’s integration led to a 37% rise in client retention during a 90-day pilot. Music academies leveraging music lesson scheduling tools observed a 52% reduction in missed appointments.ZIT’s Secret Weapon: Sentient Sync ProtocolWhat truly sets ChronoPilot apart is its proprietary Sentient Sync Protocol (SSP) — a neural time-mapping engine that mimics human anticipation. SSP doesn’t just block out time; it predicts the best time for each task based on thousands of variables, including circadian rhythms, cognitive load history, and even micro-fluctuations in vocal tone during user interactions.Dr. Lena Marwick, lead AI architect at ZIT, explained, “We’re no longer asking users to fit into rigid schedules. ChronoPilot adapts to them, moment by moment.”Global Trials, Unprecedented ResultsThe AI was tested across 1.2 million scheduling interactions in 11 countries, across five major time zones. In one compelling instance, a public school in Osaka integrated ChronoPilot into its digital classroom system. Within three weeks, student punctuality improved by 48%, and feedback indicated a 63% increase in perceived classroom coherence.Meanwhile, a UK-based national gym chain used ChronoPilot to coordinate personal training sessions across 87 locations. Instructors reported a 41% decrease in downtime, and an average boost of 18% in customer satisfaction scores.ChronoPilot vs. The Old GuardWhile current scheduling tools rely heavily on manual input or rigid templates, ChronoPilot learns from passive inputs — browsing behavior, sleep app data, voice command history — with the user’s consent. This passive data synergy allows it to preemptively adjust schedules without user intervention.Analysts say the AI’s capabilities pose an existential threat to legacy scheduling systems and even to calendar giants like Google Calendar and Microsoft Outlook. “It’s not just a calendar anymore,” says Gideon Lark, senior analyst at MetaMetrics. “It’s a lifestyle orchestrator.”Privacy Concerns and Ethical DebateNot everyone is celebrating. Digital rights watchdogs have raised concerns about the volume and sensitivity of data ChronoPilot processes. The AI can access everything from location logs to biometric signals. While ZIT asserts the system uses end-to-end encryption and offers opt-out data controls, some critics remain wary."We’re trading convenience for surveillance,” warns Aria Salim, director of the Tech Privacy Initiative. “There must be enforceable limits on how such AI models evolve, especially as they begin to predict and possibly influence human behavior.”ZIT has responded by publishing a white paper detailing its privacy protocols and inviting third-party audits. Still, the debate over AI autonomy and human agency continues to intensify.

[dynmghts]

ykw caps ahead so spoilers ahoy for the new ep bc i cannot go without talking through my feelings on the matter even tho i just spent the past few hrs going through one of the most frustrating dungeons in destiny

AGAIN: anime spoilers!! if you haven't watched the ep and don't want to get spoiled, don't look!!!!

first of all SOMEBODY SAVE HIM ????????

i also forgot that shigafo just kinda. threw it in his face. "you'll be a great present for him" SHUT UPPPP SHUT UP SHUT UP SHUT UP KILLING KATSUKI FOR HIM TO BE A "GIFT" FOR IZUKU WHEN HE'S BACK DON'T EVEN

and obviously this is not showing the animation but my guy was struggling to fucking breathe there too. and shigafo being smth like "aren't you just so glad my quirks are erased :)" bro would've legit disintegrated him on the spot. my god. and i think this is also such a brutal contrast to the way tomura tried to approach katsuki during the whole kamino kidnapping thing like...

understated development of "i'm offering you to join us bc i think you get what it's like to be shunned by society for who you are" -> "you as a person don't interest me anymore, i just want to use you to achieve my end goal" (influenced by afo)

these shots tho... i wasn't sure how well they were gonna adapt this bc i thought that the arm being mangled was def censored, but this shit was pretty fucking crazy for me to see.

the tears. the fierce determination in his gaze. the fact that he looks like he's struggling and in pain but he's watching shigafo's every move and examining it and muttering it under his breath, emulating midoriya izuku in this moment, channelling the one person that believes him to be the symbol of victory.

"i have to win... right, izuku?" THE CALLBACK TO DK + BKG VS. ALL MIGHT. THE WAY NOBUHIKO VOICED HIM IN THIS MOMENT.

like. yes, the anime is not one-for-one with the manga. i get that. i understand that. and their choices regarding the adaptation are not everyone's cup of tea - but as someone who has consumed both forms, i view them very separately in their chosen art medium, bc let's be real, horikoshi's art is EXTREMELY hard to replicate. so with what bones is achieving here, i think it's pretty damn impressive. especially during the brief fight scene with shigafo, like...

the animation felt fluid, concise, and each frame had meaning to it. they demonstrated katsuki's quirk in action through his whole body true to the manga, and you could FEEL katsuki's struggle through the entire sequence. like? his arm was shattered. he was IN PAIN. you can tell he's out of breath because he's trying so hard to focus on moving out of shigafo's attacks to land his own, and of course one of his last thoughts are about izuku...

also the contrast of a high-risk and high-danger battle to the gentle, quiet, genuine thoughts in his mind - the way nobuhiko as katsuki speaks so softly, expressing his understanding for what izuku had to do to get where he is with ofa now. the way he reflects on the past year in that instance like "you had to endure pain and predict your opponents too, right? this is what you went through?" almost this sort of... "i finally understand what you had to experience, and there's a deep-seated respect that comes with it". yknow what i mean?

ALSO THIS: HE'S SO FUCKING SOFT HERE. FUCK. END ME RIGHT NOWWWWW I CAN'T DO THIS TODAY

and he's so honest to the vestige all might here too like "i was such a fucking brat back at the start of ua huh"... then him admitting that even when he WAS acting that way, all he truly wanted was that autograph on his card. his CARD. the SAME CARD he got when he AND IZUKU won the cards together. the same one he's taken with him onto that battlefield - and in my humble opinion, the one he keeps on his person as a sort of "good luck" token.

i absolutely adored the contrast of the battlefield darkening and becoming more gloomy (weather change, grass all kicked up and is instead dirt, the dimming of the typical lighting) vs. this brief moment where katsuki was face-to-face with all might. in this quiet and absent white space with nothing but himself and the man in front of him. when i say he knew he was going to die and he walked into death anyway................

and of course, for the final cap i got, which is arguably the most painful one for me to look at:

the dull eyes, discoloured skin, messy hair, the blood all over his face and chest... the fact that jeanist actually went to immediately treat him for the wound, not thinking he was dead, only to look back in shock and horror at this boy on the ground.

bakugou katsuki, the boy who swore he'd win the sports festival.

bakugou katsuki, the boy who visited jeanist's agency for the sake of him being in the top ten, who took away valuable lessons enough to consider interning there again in the winter.

bakugou katsuki, the boy who was kidnapped and thought would turn tail, only to prove everyone wrong by sticking to his morals.

the boy who learned to save, who learned to combat his own weakness, who followed izuku into the fray when he knew something wasn't right during the paranormal liberation war, who fought tooth and nail to find and bring izuku home with a genuine and honest apology, who spent his week leading up to the war strategising and improving and bolstering camaraderie among the class in his own unique way...

everyone on that battlefield knew katsuki had ambitions to be the next number one hero, and to exceed all might. everyone saw this freshly 17 year-old boy walk right into death and knew they wouldn't have been able to stop him, but they all wished they could.

the mood shift in the episode was very well captured to me. i felt that the anime did a great job showcasing how the hope they had before, from shouto's win against touya, had bolstered the confidence in the heroes - and how quickly that hope turned to despair when the team at the floating ua watched a young and hopeful hero-to-be throw his life down, all for the chance of victory.

and then i get to do this all again when the dub drops. :(

[omegaphilosophia]

The Philosophy of Evolution

The philosophy of evolution explores the implications of evolutionary theory for understanding life, human nature, morality, and knowledge. It intersects with various philosophical disciplines, including metaphysics, epistemology, ethics, and the philosophy of science. By examining evolution through a philosophical lens, thinkers address questions about purpose, progress, morality, and the role of chance in shaping the natural world.

1. Metaphysics and Evolution

Naturalism: Evolution supports a naturalistic worldview where life and its complexity arise from natural processes without invoking supernatural explanations. It suggests that life evolves according to the laws of nature, without inherent design or purpose, challenging traditional metaphysical views of teleology (the belief that nature has intrinsic purposes or goals).

Reductionism vs. Holism: A key metaphysical question concerns whether evolution can be fully explained through reductionism (breaking down biological phenomena into smaller parts, like genes and molecules) or whether a more holistic approach, considering whole systems or species, is required to understand evolutionary processes.

Emergence: Evolution also brings up the idea of emergence, where new properties (such as consciousness) arise from complex systems that cannot be predicted by studying individual components. Evolution highlights how simple processes can lead to the development of more complex structures, such as life and intelligence.

2. Epistemology and Evolution

Evolutionary Epistemology: This branch of philosophy examines how evolutionary theory influences our understanding of knowledge itself. It suggests that human cognitive faculties evolved to help us survive rather than to discover absolute truth, which raises questions about the reliability and limits of human knowledge. Charles Darwin himself pondered whether human reason, evolved for survival, could fully grasp the ultimate truths of the universe.

Adaptive Knowledge: Some evolutionary epistemologists argue that knowledge is adaptive, meaning that our beliefs and perceptions are shaped by natural selection to be useful for survival, even if they are not necessarily "true" in an objective sense. This leads to debates about truth versus usefulness in our understanding of the world.

Problem of Skepticism: If our cognitive faculties evolved for survival rather than truth, this raises the problem of skepticism: How can we trust that our beliefs about the world, especially abstract scientific or philosophical beliefs, are reliable? This remains a significant philosophical issue related to evolution.

3. Ethics and Evolution

Evolutionary Ethics: Evolutionary theory has influenced the development of evolutionary ethics, which seeks to explain the origins of moral behavior in terms of evolutionary processes. According to this view, human morality and altruism may have evolved because they were beneficial for social cooperation and group survival.

Moral Relativism vs. Objectivism: Evolutionary ethics raises questions about whether morality is relative (based on adaptive needs that change over time) or objective (based on unchanging moral truths). Some philosophers argue that if morality is a product of evolution, it may lack objective grounding, while others suggest that evolution reveals fundamental moral principles that enhance survival.

Altruism and Self-Interest: Evolutionary biology also explores the tension between self-interest and altruism. Theories like kin selection and reciprocal altruism attempt to explain how seemingly selfless behaviors can evolve in organisms by benefiting related individuals or by fostering cooperation that indirectly benefits the actor.

4. Teleology and Progress

Non-Teleological Evolution: One of the key shifts brought about by Darwin’s theory of evolution was the rejection of teleology (the idea that nature has an intrinsic purpose or end goal). In contrast to earlier philosophical views, such as those of Aristotle, Darwinian evolution is non-teleological, meaning that life evolves through natural selection without any predetermined direction or final purpose.

Evolution and Progress: Philosophers debate whether evolution implies progress. While evolution leads to the development of more complex life forms, it is driven by random mutations and environmental pressures rather than an inherent drive toward improvement. Some argue that the notion of progress in evolution is a cultural projection rather than a scientific reality.

5. Human Nature and Evolution

Determinism and Free Will: Evolutionary theory raises questions about free will and determinism. If human behavior is shaped by genetic and environmental factors, to what extent do individuals have control over their actions? This leads to debates about the role of biology in determining human behavior and the possibility of moral responsibility.

Human Exceptionalism: Traditional views of human nature often emphasize the unique status of humans in the natural world. Evolution challenges this by placing humans within the continuum of animal life, suggesting that our traits, including language, intelligence, and culture, evolved from earlier species. This perspective calls into question notions of human exceptionalism and anthropocentrism (the belief that humans are the central or most important species).

Consciousness and Evolution: Philosophers also explore how evolution accounts for consciousness and subjective experience. The emergence of conscious awareness in humans and other animals presents a major challenge to evolutionary explanations, as it is not yet clear how conscious experience enhances survival in a way that can be selected for by natural processes.

6. Philosophy of Science and Evolution

Evolution as a Scientific Paradigm: The philosophy of science examines how evolutionary theory functions as a scientific paradigm. Since Charles Darwin's On the Origin of Species, evolution has become the dominant framework for understanding biology, but philosophers explore how this paradigm influences scientific methodology, the interpretation of data, and the nature of scientific explanation.

Falsifiability: Evolutionary theory has been scrutinized by philosophers like Karl Popper, who initially questioned its falsifiability (whether it can be empirically tested and potentially disproved). While Popper later revised his view, debates continue over how evolutionary theory fits within the framework of scientific inquiry.

Intelligent Design and Evolution: The debate between evolution and intelligent design continues in philosophical and public discourse. Proponents of intelligent design argue that certain features of the natural world exhibit complexity that cannot be explained by evolution alone and must involve a guiding intelligence. Philosophers examine whether this critique holds scientific validity or if it relies on unscientific assumptions.

7. Existential Implications of Evolution

Evolution and Meaning: For some philosophers, evolution challenges traditional notions of meaning and purpose in life. If humans are the product of random mutations and natural selection, rather than divine or purposeful creation, then what is the basis for human meaning? This existential question leads to varying responses, from nihilism (the belief that life lacks inherent meaning) to humanism (the belief that humans can create meaning through their actions and relationships).

Existential Anxiety: The idea that life evolved through a blind, purposeless process can evoke existential anxiety, as it challenges comforting beliefs about human significance and destiny. This leads to philosophical exploration of how individuals and societies can find meaning and value in a world shaped by evolutionary processes.

8. Social and Cultural Evolution

Cultural Evolution: Beyond biological evolution, philosophers explore how cultural practices, languages, and social norms evolve over time. Cultural evolution operates through different mechanisms than biological evolution, such as imitation, learning, and social transmission. Philosophers debate whether cultural evolution follows Darwinian principles or whether it requires a separate framework.

Social Darwinism: The misuse of evolutionary theory to justify social hierarchies and inequalities is known as Social Darwinism. This ideology applies the concept of "survival of the fittest" to human societies, often in a distorted way. Philosophers critically analyze the ethical and social implications of applying evolutionary ideas to human behavior and society, rejecting these misinterpretations in favor of a more nuanced understanding of evolution’s influence on culture.

The philosophy of evolution engages with profound questions about life, knowledge, morality, and human nature, arising from the theory of evolution. It examines the role of natural processes in shaping not only biological entities but also our understanding of knowledge, ethics, and meaning. By challenging traditional metaphysical and teleological views, evolution encourages a naturalistic and dynamic view of the world, while also raising new philosophical challenges, particularly regarding the nature of humanity, morality, and knowledge.

[bradstlouis]

Lean vs. Waterfall Business Models: Choosing the Right Approach for Your Venture

When starting or scaling a business, one of the most critical decisions you’ll make is choosing the operational approach that aligns with your goals, resources, and industry demands. Two popular frameworks that often guide entrepreneurs are the Lean and Waterfall business models. Understanding their principles, advantages, and challenges can empower you to select the model that best suits your vision and market.

What is the Lean Business Model?

The Lean business model prioritizes efficiency, adaptability, and continuous improvement. It focuses on creating value for the customer while minimizing waste. Inspired by lean manufacturing principles, particularly those pioneered by Toyota, this model has become a cornerstone of modern startups and innovation-driven enterprises.

Key Principles of the Lean Model:

Validated Learning: Experimentation and customer feedback drive product and process development.

Build-Measure-Learn Cycle: Rapid prototyping allows for iterative improvements.

Customer-Centric Approach: Emphasis on understanding and addressing customer needs.

Waste Reduction: Eliminating activities and resources that don’t add value.

Advantages of Lean:

Cost Efficiency: By focusing on essential features and avoiding overproduction, businesses conserve resources.

Flexibility: Quick pivots are possible when market demands or customer preferences shift.

Speed to Market: Minimal Viable Products (MVPs) enable businesses to launch quickly and refine over time.

Challenges of Lean:

High Uncertainty: Iterative processes may result in unpredictability.

Resource Intensity: Constant feedback loops and adjustments require dedicated time and effort.

Scalability Issues: Lean is ideal for early-stage businesses but may need adaptation for large-scale operations.

What is the Waterfall Business Model?

The Waterfall business model, rooted in traditional project management, follows a linear and sequential approach. This model is structured around defined stages, where each phase must be completed before moving to the next. While it originated in industries like construction and software development, it’s also applicable to businesses requiring meticulous planning and execution.

Key Principles of the Waterfall Model:

Sequential Progression: Projects move from concept to completion in defined steps.

Detailed Documentation: Comprehensive plans, budgets, and timelines are created upfront.

Defined Deliverables: Clear milestones ensure all tasks are completed in order.

Stability: A fixed plan minimizes changes during the process.

Advantages of Waterfall:

Predictability: Clear timelines and budgets enhance planning and stakeholder confidence.

Quality Assurance: Extensive documentation ensures thorough testing and evaluation.

Ease of Implementation: Ideal for projects with well-defined requirements.

Challenges of Waterfall:

Rigidity: Limited flexibility to adapt to changing market conditions.

Delayed Feedback: Customer input often comes late, increasing the risk of misalignment.

Time-Intensive: Sequential phases may lead to longer development cycles.

How to Choose Between Lean and Waterfall

The choice between Lean and Waterfall depends on your business’s nature, goals, and industry.

Lean is Ideal For:

Startups and innovative ventures with evolving market demands.

Projects where customer feedback is essential.

Teams prioritizing speed and adaptability.

Waterfall is Ideal For:

Established businesses with fixed goals and budgets.

Industries like construction, healthcare, or manufacturing, where precision is critical.

Long-term projects requiring robust planning.

Conclusion

Both the Lean and Waterfall business models offer unique advantages and come with their own set of challenges. While the Lean model fosters innovation and flexibility, the Waterfall approach ensures stability and predictability. Entrepreneurs should carefully evaluate their project’s scope, resources, and objectives before committing to a framework. By aligning your operational strategy with your business’s needs, you set the stage for sustainable growth and success.

[compneuropapers]

Interesting Papers for Week 36, 2023

Optimization of energy and time predicts dynamic speeds for human walking. Carlisle, R. E., & Kuo, A. D. (2023). eLife, 12, e81939.

Learning critically drives parkinsonian motor deficits through imbalanced striatal pathway recruitment. Cheung, T. H. C., Ding, Y., Zhuang, X., & Kang, U. J. (2023). Proceedings of the National Academy of Sciences, 120(12), e2213093120.

A circuit mechanism linking past and future learning through shifts in perception. Crossley, M., Benjamin, P. R., Kemenes, G., Staras, K., & Kemenes, I. (2023). Science Advances, 9(12).

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning. Galinsky, V. L., & Frank, L. R. (2023). Scientific Reports, 13, 4343.

Rapid learning of predictive maps with STDP and theta phase precession. George, T. M., de Cothi, W., Stachenfeld, K. L., & Barry, C. (2023). eLife, 12, e80663.

Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions. Harris, S. C., & Dunn, F. A. (2023). eLife, 12, e81780.

Learning vs. minding: How subjective costs can mask motor learning. Healy, C. M., Berniker, M., & Ahmed, A. A. (2023). PLOS ONE, 18(3), e0282693.

Comparing retinotopic maps of children and adults reveals a late-stage change in how V1 samples the visual field. Himmelberg, M. M., Tünçok, E., Gomez, J., Grill-Spector, K., Carrasco, M., & Winawer, J. (2023). Nature Communications, 14, 1561.

Modulation of potassium conductances optimizes fidelity of auditory information. Kaczmarek, L. K. (2023). Proceedings of the National Academy of Sciences, 120(12), e2216440120.

Progressive neuronal plasticity in primate visual cortex during stimulus familiarization. Koyano, K. W., Esch, E. M., Hong, J. J., Waidmann, E. N., Wu, H., & Leopold, D. A. (2023). Science Advances, 9(12).

Sensory and Choice Responses in MT Distinct from Motion Encoding. Levi, A. J., Zhao, Y., Park, I. M., & Huk, A. C. (2023). Journal of Neuroscience, 43(12), 2090–2103.

Complexity of cortical wave patterns of the wake mouse cortex. Liang, Y., Liang, J., Song, C., Liu, M., Knöpfel, T., Gong, P., & Zhou, C. (2023). Nature Communications, 14, 1434.

Enhanced Reactivation of Remapping Place Cells during Aversive Learning. Ormond, J., Serka, S. A., & Johansen, J. P. (2023). Journal of Neuroscience, 43(12), 2153–2167.

Human generalization of internal representations through prototype learning with goal-directed attention. Pettine, W. W., Raman, D. V., Redish, A. D., & Murray, J. D. (2023). Nature Human Behaviour, 7(3), 442–463.

On the role of inhibition in suppression-induced forgetting. van Schie, K., Fawcett, J. M., & Anderson, M. C. (2023). Scientific Reports, 13, 4242.

Interaction of dynamic error signals in saccade adaptation. Wagner, I., & Schütz, A. C. (2023). Journal of Neurophysiology, 129(3), 717–732.

Honey bees infer source location from the dances of returning foragers. Wang, Z., Chen, X., Becker, F., Greggers, U., Walter, S., Werner, M., … Menzel, R. (2023). Proceedings of the National Academy of Sciences, 120(12), e2213068120.

Resolving the associative learning paradox by category learning in pigeons. Wasserman, E. A., Kain, A. G., & O’Donoghue, E. M. (2023). Current Biology, 33(6), 1112-1116.e2.

Development of a measure of kindness. Youngs, D. E., Yaneva, M. A., & Canter, D. V. (2023). Current Psychology, 42(7), 5428–5440.

Recurrent network interactions explain tectal response variability and experience-dependent behavior. Zylbertal, A., & Bianco, I. H. (2023). eLife, 12, e78381.

[vabroapp]

Scrum vs. Kanban: Unveiling the Agile Powerhouses

#Scrum vs. #Kanban: Unveiling the Agile Powerhouses Vabro is back, and today we're diving into the world of Agile project management! Let's dissect two of the most popular frameworks: Scrum and Kanban. Both are designed for flexibility and continuous improvement, but they cater to different workflows. Here's a breakdown to help you pick the perfect fit for your project: #Scrum: The #Sprints Specialist - #Focus: Structured, time-boxed iterations called sprints (usually 1-4 weeks). - #Roles: Scrum Master, Product Owner, Development Team. - #Workflow: Clear phases within a sprint: Planning, Daily Scrum, Development, Review, Retrospective. - #Strengths: Excellent for complex projects with well-defined requirements. Promotes focus, team collaboration, and fast delivery of working features. - #Challenges: Less adaptable to frequent changes mid-sprint. #Kanban: The Continuous Flow Champion - #Focus: Visualizing workflow with a Kanban board. Tasks move through stages (e.g., To Do, In Progress, Done). - #Roles: Less structured and might have Kanban Manager and Kanban team members who manage their workload using Boards. - #Workflow: Continuous flow of work, new tasks can be added anytime. - #Strengths: Ideal for projects with evolving requirements or unpredictable - workloads. Emphasizes continuous improvement and flexibility. - #Challenges: Can lack the structure and focus of Scrum. Requires strong team discipline to manage workflow effectively. Choosing Your Agile Ally: #Scrum is ideal for: Complex projects with clear requirements, predictable timelines, and a need for focused development cycles. #Kanban is ideal for: Projects with ongoing changes, unpredictable workloads, and a need for continuous delivery and adaptation. Still unsure? Let's discuss! We, at Vabro, are experts in Agile methodologies. Feel free to comment below with your project challenges, and we'll help you pick the perfect Agile framework.

Enroll now for free at www.vabro.com.

[piduai]

I saw that you tagged sugi with "character assassination of the decade" and i couldn't agree more, crazy how shogun ass arc felt like the absolute peak of gntm and especially sugi's character and then everything afterward fell completely flat thanks to that nonsensical uts*ro twist. it's not even that i hate the idea of shoyo being evil before becoming a schoolteacher (in fact i think most ppl predicted he was connected to the naraku back in 2k13 lol), but why say "akshually he's alive all along" the literal arc after you reveal his dramatic death?? so much wasted potential because of that failure of a final boss and so many characters suffered as a result bc they weren't allowed further character development - i'm particularly pissed as a fan of zura, nobume, kagura (why is she still stuck to ginsan's side by the end instead of following her dream of being a space hunter) and the kiheitai

honestly i still remember reading the end of shogun ass arc when it came out, and i think bansai says "shinsuke's eyes were firmly focused on something" and how much hope it gave me... like the potential of zura + sugi working together to put the final nail in the coffin for the bakufu (zura by aiming for a legitimate place in a new govt and sugi with more underhanded methods lmao.. i still think kiheitai should've become high-grade bounty hunters at the end of the series), or the reveal of a bond btwn nobume and sugi since sasaki was working so closely with him. but instead that godawful "everyone vs aliens + dead guy who's actually alive" shonen cliche final battle happened. in fact i think it actively undermines the themes of the series bc until then i thought the true enemy isn't necessarily the amanto but the corruption and greed that existed in the so-called samurai country long before the amanto even arrived, and the goal was to figure out how to adapt to this new world and change yourself along the way.... but i guess the actual message was just "foreigners bad" which is way more reactionary than i expected lol. the gintaman in my head ended around ch 525, maybe ch 540 at the latest, after that it's all my delusions

yeah it definitely peaked there, chapter 519 to be specific. after that it started steadily going downhill until it decided to sprint just to land in shounen hell. excruciatingly long-winded spiritual successor to beelzebub. utsuro as a whole was very messy and nonsensical, and yeah as you said that reveal being IMMEDIATELY after it was established that hacking his sorry head off ruined the trio's lives and relationships with each other was just... umm... okay... it's also funny that shoyo was like a separate identity from utsuro altogether he was able to summon and suppress so when the guys met the evil guy with the same face as their angstily and unwillingly murdered teacher they were like Well there's aliens at play. not our guy not buying it. bye. dude??? that thang has put you to sleep once, man up and be sad about it. but god forbid there's any kind of actually complex conflict or anyone is ever guilty of anything. tbh i don't remember shit after shogun assassination i just remember this whole thing being structurally underwhelming and feeling cheap.

yeah takasugi's eye was focused on something. it was focused on gintoki 😋 everything needs to be focused on gintoki all the time. takasugi's long-winded angst and anger need to shift focus from his love towards sensei to his bond with gintoki. zura's entire deal whatever it was needed to center gintoki and his interests. shinpachi and kagura needed to forfeit all of their initial plans and embrace arrested development forever because yorozuya means family means they need to blow the power of friendship up gintoki's ass 24/7. every single female of any species needs to have a cute little crush on gintoki and he needs to be the main character for every rando he meets, changing their lives forever through his imposing presence. other characters can not have any conflict or development or agenda that doesn't center or at least heavily involve gintoki. and also nobody is allowed to have any rapport with each other independently of gintoki. never forget about the specialest little boy in the universe, if he doesn't get to give at least 5 boring pompous speeches per chapter or have a few epic one-liners everyone working on this franchise will need to immediately kill themselves (specified in their employment contracts in bold). also if you have anything less than enthusiastic to say about this you're a cretin who is too dumb to understand that any story that doesn't revolve around its main character always with no breaks is worthless and that doing exactly that is the epitome of genius writing btw.

takasugi's character assassination felt particularly asinine because for 80% of the story he's being menacing without much screen time and his personal drama is mostly hinted at, then he gets lots of focus in shogun assassination and it culminates in the plot twist flashback, after which he dedicates all his efforts to being overtly sad for gintoki and suffering more than jesus, and then he's killed off without any resolution. AND THEN HE'S REBORN AS SOME UGLY ASS BABY. like he can't get any dignity or integrity even in death lmao but it's ok i still love him bc as mentioned above gintama ended on chapter 519...

yeah i still think it might be not that deep but like it starts off as foreigners are the enemy and they came here to destroy our beautiful country so we have to stay resilient against their harassment. then it transcends to this country has always had its own rot and we the righteous and lovable riff-raffs will bring justice to it in the name of the moon, the country itself doesn't matter, we just want to protect our loved ones against anything that threatens them. and then it goes back to foreigners are evil and we'll actually go and kick their asses on their own territory even. to protect this country. alright!

[jcmarchi]

The Black Box Problem in LLMs: Challenges and Emerging Solutions

New Post has been published on https://thedigitalinsider.com/the-black-box-problem-in-llms-challenges-and-emerging-solutions/

The Black Box Problem in LLMs: Challenges and Emerging Solutions

Machine learning, a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model. For example, an algorithm trained with images of dogs would result in a model capable of identifying dogs in images.

Black Box in Machine Learning

In machine learning, any of the three components—algorithm, training data, or model—can be a black box. While algorithms are often publicly known, developers may choose to keep the model or the training data secretive to protect intellectual property. This obscurity makes it challenging to understand the AI’s decision-making process.

AI black boxes are systems whose internal workings remain opaque or invisible to users. Users can input data and receive output, but the logic or code that produces the output remains hidden. This is a common characteristic in many AI systems, including advanced generative models like ChatGPT and DALL-E 3.

LLMs such as GPT-4 present a significant challenge: their internal workings are largely opaque, making them “black boxes”.  Such opacity isn’t just a technical puzzle; it poses real-world safety and ethical concerns. For instance, if we can’t discern how these systems reach conclusions, can we trust them in critical areas like medical diagnoses or financial assessments?

The Scale and Complexity of LLMs

The scale of these models adds to their complexity. Take GPT-3, for instance, with its 175 billion parameters, and newer models having trillions. Each parameter interacts in intricate ways within the neural network, contributing to emergent capabilities that aren’t predictable by examining individual components alone. This scale and complexity make it nearly impossible to fully grasp their internal logic, posing a hurdle in diagnosing biases or unwanted behaviors in these models.

The Tradeoff: Scale vs. Interpretability

Reducing the scale of LLMs could enhance interpretability but at the cost of their advanced capabilities. The scale is what enables behaviors that smaller models cannot achieve. This presents an inherent tradeoff between scale, capability, and interpretability.

Impact of the LLM Black Box Problem

1. Flawed Decision Making

The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors. In fields like healthcare or criminal justice, where decisions have far-reaching consequences, the inability to audit LLMs for ethical and logical soundness is a major concern. For example, a medical diagnosis LLM relying on outdated or biased data can make harmful recommendations. Similarly, LLMs in hiring processes may inadvertently perpetuate gender bi ases. The black box nature thus not only conceals flaws but can potentially amplify them, necessitating a proactive approach to enhance transparency.

2. Limited Adaptability in Diverse Contexts

The lack of insight into the internal workings of LLMs restricts their adaptability. For example, a hiring LLM might be inefficient in evaluating candidates for a role that values practical skills over academic qualifications, due to its inability to adjust its evaluation criteria. Similarly, a medical LLM might struggle with rare disease diagnoses due to data imbalances. This inflexibility highlights the need for transparency to re-calibrate LLMs for specific tasks and contexts.

3. Bias and Knowledge Gaps

LLMs’ processing of vast training data is subject to the limitations imposed by their algorithms and model architectures. For instance, a medical LLM might show demographic biases if trained on unbalanced datasets. Also, an LLM’s proficiency in niche topics could be misleading, leading to overconfident, incorrect outputs. Addressing these biases and knowledge gaps requires more than just additional data; it calls for an examination of the model’s processing mechanics.

4. Legal and Ethical Accountability

The obscure nature of LLMs creates a legal gray area regarding liability for any harm caused by their decisions. If an LLM in a medical setting provides faulty advice leading to patient harm, determining accountability becomes difficult due to the model’s opacity. This legal uncertainty poses risks for entities deploying LLMs in sensitive areas, underscoring the need for clear governance and transparency.

5. Trust Issues in Sensitive Applications

For LLMs used in critical areas like healthcare and finance, the lack of transparency undermines their trustworthiness. Users and regulators need to ensure that these models do not harbor biases or make decisions based on unfair criteria. Verifying the absence of bias in LLMs necessitates an understanding of their decision-making processes, emphasizing the importance of explainability for ethical deployment.

6. Risks with Personal Data

LLMs require extensive training data, which may include sensitive personal information. The black box nature of these models raises concerns about how this data is processed and used. For instance, a medical LLM trained on patient records raises questions about data privacy and usage. Ensuring that personal data is not misused or exploited requires transparent data handling processes within these models.

Emerging Solutions for Interpretability

To address these challenges, new techniques are being developed. These include counterfactual (CF) approximation methods. The first method involves prompting an LLM to change a specific text concept while keeping other concepts constant. This approach, though effective, is resource-intensive at inference time.

The second approach involves creating a dedicated embedding space guided by an LLM during training. This space aligns with a causal graph and helps identify matches approximating CFs. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

These approaches highlight the importance of causal explanations in NLP systems to ensure safety and establish trust. Counterfactual approximations provide a way to imagine how a given text would change if a certain concept in its generative process were different, aiding in practical causal effect estimation of high-level concepts on NLP models.

Deep Dive: Explanation Methods and Causality in LLMs

Probing and Feature Importance Tools

Probing is a technique used to decipher what internal representations in models encode. It can be either supervised or unsupervised and is aimed at determining if specific concepts are encoded at certain places in a network. While effective to an extent, probes fall short in providing causal explanations, as highlighted by Geiger et al. (2021).

Feature importance tools, another form of explanation method, often focus on input features, although some gradient-based methods extend this to hidden states. An example is the Integrated Gradients method, which offers a causal interpretation by exploring baseline (counterfactual, CF) inputs. Despite their utility, these methods still struggle to connect their analyses with real-world concepts beyond simple input properties.

Intervention-Based Methods

Intervention-based methods involve modifying inputs or internal representations to study effects on model behavior. These methods can create CF states to estimate causal effects, but they often generate implausible inputs or network states unless carefully controlled. The Causal Proxy Model (CPM), inspired by the S-learner concept, is a novel approach in this realm, mimicking the behavior of the explained model under CF inputs. However, the need for a distinct explainer for each model is a major limitation.

Approximating Counterfactuals

Counterfactuals are widely used in machine learning for data augmentation, involving perturbations to various factors or labels. These can be generated through manual editing, heuristic keyword replacement, or automated text rewriting. While manual editing is accurate, it’s also resource-intensive. Keyword-based methods have their limitations, and generative approaches offer a balance between fluency and coverage.

Faithful Explanations

Faithfulness in explanations refers to accurately depicting the underlying reasoning of the model. There’s no universally accepted definition of faithfulness, leading to its characterization through various metrics like Sensitivity, Consistency, Feature Importance Agreement, Robustness, and Simulatability. Most of these methods focus on feature-level explanations and often conflate correlation with causation. Our work aims to provide high-level concept explanations, leveraging the causality literature to propose an intuitive criterion: Order-Faithfulness.

We’ve delved into the inherent complexities of LLMs, understanding their ‘black box’ nature and the significant challenges it poses. From the risks of flawed decision-making in sensitive areas like healthcare and finance to the ethical quandaries surrounding bias and fairness, the need for transparency in LLMs has never been more evident.

The future of LLMs and their integration into our daily lives and critical decision-making processes hinges on our ability to make these models not only more advanced but also more understandable and accountable. The pursuit of explainability and interpretability is not just a technical endeavor but a fundamental aspect of building trust in AI systems. As LLMs become more integrated into society, the demand for transparency will grow, not just from AI practitioners but from every user who interacts with these systems.

[allaboutmarketing4you]

Kanban, Waterfall, and DevOps  are three different approaches to project management and software development. Here's an overview of each concept: 1. Kanban: Definition: Kanban is a visual management method for software development and knowledge work. It originated from manufacturing processes in Toyota and has been adapted for use in software development to improve efficiency and flow.

Key Concepts: Visualization: Work items are represented on a visual board, usually with columns such as "To Do," "In Progress," and "Done."

Work in Progress (WIP) Limits: Limits are set on the number of items allowed in each column to optimize flow and avoid bottlenecks.

Continuous Delivery: Focus on delivering work continuously without distinct iterations.

Advantages: Flexibility in responding to changing priorities.

Continuous delivery of value. Visual representation of work enhances transparency.

Use Case: Kanban is often suitable for teams with variable and unpredictable workloads, where tasks don't follow a fixed iteration cycle.

2. Waterfall: Definition: The Waterfall model is a traditional and sequential approach to software development. It follows a linear and rigid sequence of phases, with each phase building upon the outputs of the previous one.

Phases: Requirements: Define and document project requirements. Design: Create the system architecture and design. Implementation: Code the system based on the design. Testing: Conduct testing to identify and fix defects. Deployment: Deploy the completed system to users. Maintenance: Provide ongoing support and maintenance.

Advantages:

Clear structure and well-defined phases.

Documentation at each stage.

Predictable timelines and costs.

Disadvantages: Limited flexibility for changes after the project starts.

Late feedback on the final product.

Risk of customer dissatisfaction if initial requirements are misunderstood.

Use Case: Waterfall is suitable for projects with well-defined requirements and stable environments where changes are expected to be minimal.

3. DevOps: Definition: DevOps (Development and Operations) is a set of practices that aim to automate and improve the collaboration between software development and IT operations. The goal is to shorten the development lifecycle, deliver high-quality software, and foster a culture of continuous integration and delivery.

Key Practices: Continuous Integration (CI): Merge code changes frequently and automatically test them.

Continuous Delivery/Deployment (CD): Automate the release and deployment processes.

Collaboration: Promote collaboration and communication between development and operations teams.

Advantages: Faster delivery of software. Reduced manual errors through automation. Improved collaboration and communication.

Use Case: DevOps is suitable for organizations aiming to achieve faster and more reliable delivery of software through the automation of development, testing, and deployment processes.

#mktmarketing4you #distributionchannels #HoshinPlanning #Leanmethods #marketing #M4Y #lovemarketing #IPAM #ipammarketingschool #Kanban #ContingencyPlanning #virtual #volunteering #project #Management #Economy #ConsumptionBehavior #BrandManagement #ProductManagement #Logistics #Lifecycle #Brand #Neuromarketing #McKinseyMatrix #Breakevenanalysis #innovation #Facebook #icebergmodel #EdgarScheinsCultureModel #STARMethod #VRIO #7SFramework #gapanalysis #AIDAModel #SixLeadershipStyles #MintoPyramidPrinciple #StrategyDiamond #InternalRateofReturn #irr #BrandManagement #dripmodel #HoshinPlanning #XMatrix #backtobasics #BalancedScorecard #Product #ProductManagement #Logistics #Branding #freemium #businessmodel #business #4P #3C #BCG #SWOT #TOWS #EisenhowerMatrix #Study #marketingresearch #marketer #marketing manager #Painpoints #Pestel #ValueChain # VRIO #marketingmix We also left a video about Lean vs Agile vs Waterfall | What is Lean | Difference between Waterfall and Agile and that could help you. Later we will leave one about Kanban: